Digital Twin Strategies in Global Retail: Key Developments and Strategies (pre-order)

As digital twin technology matures, its application in the retail sector is expected to reach nearly USD 10 billion by 2030. By bridging physical and virtual environments through spatial computing, digital twins enhance decision-making efficiency and operational quality. This report analyzes the strategic approaches of leading global players in digital twin retail applications, offering insights for companies seeking entry points into this emerging field.

Table of Contents

1. Global Trends in Digital Twin Adoption in Retail

1.1 The Path to Omnichannel: Opportunities for Digital Twin Applications in Retail

1.1.1 Integration Challenges: The Complexity of Holistic Customer Profiling

1.1.2 Simulation Capabilities: Reimagining Store Management and Customer Experience

1.1.3 Predictive Insights: Balancing Customer Experience and Operational Efficiency

1.2 The Three Stages of Digital Twin Platform Development: Modeling, Integration, and Predictive Analytics

1.3 Key Applications of Digital Twins in Retail

1.3.1 Application Focus #1: Strengthening the Decision-Making Foundation in Retail

1.3.2 Application Focus #2: Enhancing Omnichannel Customer Experience

1.4 The Global Digital Twin Retail Ecosystem Is Taking Shape

1.4.1 The Rise of Digital Twin Retail: A Global Submarket Collaboration Approach

1.4.2 Key Players with Market Potential in Digital Twin Retail

2. Strategies of Leading Industry Players

2.1 Product Strategy

2.1.1 Basic Platform: Customization, 3D Modeling & AI Expansion

2.1.2 Value-Added Services: VR/AR Integration & Retail Connectivity

2.2 Data Integration Strategies

2.2.1 Basic Platform: Features Unified 3D Data Formats and AI-Integrated Development

2.2.2 Value-Added Services: Focuses on Integrating And Analyzing Retail Commerce Data

3. Key Case Studies on Digital Twin Adoption in Retail

3.1 Case 1: Nvidia Uses a Customized Platform and Retail Data for Diverse Simulations

3.2 Case 2: Matterport Integrates AI Multimodal Tech to Speed Up Data Processing

3.3 Case 3: Treedis Utilizes VR and AR to Create an Intuitive Interface, Reducing Planning Time

3.4 Case 4: Treedis Integrates Commerce Tools to Enhance the Online Shopping Experience

4. MIC Perspective

Appendix

List of Companies

List of Tables

Table 1: Key Players in the Global Digital Twin Retail Ecosystem

Table 2. Potential Data Requirements for Digital Twin Applications in Retail

Table 3: Comparative Analysis of Key Cases

List of Figures

Figure 1: Global Digital Twin Retail Market Size, 2023–2030

Figure 2: The Three Phases of Digital Twin Platform Evolution

Figure 3: Global Digital Twin Retail: The Three Key Submarket Divisions

Figure 4: Product Strategy Comparison of Key Players in the Basic Platform Sector

Figure 5: Product Strategy Comparison of Key Players in the Value-Added Services Sector

Figure 6: Implementation Results at Lowe (USA)

Figure 7: Implementation Results at Tsuen Wan Plaza (Hong Kong)

Figure 8: Implementation Results at Trax Retail (Singapore)

Figure 9: Implementation Results at Optical Center

Table of Contents

1. Global Trends in Digital Twin Adoption in Retail

1.1 The Path to Omnichannel: Opportunities for Digital Twin Applications in Retail

1.1.1 Integration Challenges: The Complexity of Holistic Customer Profiling

1.1.2 Simulation Capabilities: Reimagining Store Management and Customer Experience

1.1.3 Predictive Insights: Balancing Customer Experience and Operational Efficiency

1.2 The Three Stages of Digital Twin Platform Development: Modeling, Integration, and Predictive Analytics

1.3 Key Applications of Digital Twins in Retail

1.3.1 Application Focus #1: Strengthening the Decision-Making Foundation in Retail

1.3.2 Application Focus #2: Enhancing Omnichannel Customer Experience

1.4 The Global Digital Twin Retail Ecosystem Is Taking Shape

1.4.1 The Rise of Digital Twin Retail: A Global Submarket Collaboration Approach

1.4.2 Key Players with Market Potential in Digital Twin Retail

2. Strategies of Leading Industry Players

2.1 Product Strategy

2.1.1 Basic Platform: Customization, 3D Modeling & AI Expansion

2.1.2 Value-Added Services: VR/AR Integration & Retail Connectivity

2.2 Data Integration Strategies

2.2.1 Basic Platform: Features Unified 3D Data Formats and AI-Integrated Development

2.2.2 Value-Added Services: Focuses on Integrating And Analyzing Retail Commerce Data

3. Key Case Studies on Digital Twin Adoption in Retail

3.1 Case 1: Nvidia Uses a Customized Platform and Retail Data for Diverse Simulations

3.2 Case 2: Matterport Integrates AI Multimodal Tech to Speed Up Data Processing

3.3 Case 3: Treedis Utilizes VR and AR to Create an Intuitive Interface, Reducing Planning Time

3.4 Case 4: Treedis Integrates Commerce Tools to Enhance the Online Shopping Experience

4. MIC Perspective

Appendix

List of Companies

List of Tables

Table 1: Key Players in the Global Digital Twin Retail Ecosystem

Table 2. Potential Data Requirements for Digital Twin Applications in Retail

Table 3: Comparative Analysis of Key Cases

List of Figures

Figure 1: Global Digital Twin Retail Market Size, 2023–2030

Figure 2: The Three Phases of Digital Twin Platform Evolution

Figure 3: Global Digital Twin Retail: The Three Key Submarket Divisions

Figure 4: Product Strategy Comparison of Key Players in the Basic Platform Sector

Figure 5: Product Strategy Comparison of Key Players in the Value-Added Services Sector

Figure 6: Implementation Results at Lowe (USA)

Figure 7: Implementation Results at Tsuen Wan Plaza (Hong Kong)

Figure 8: Implementation Results at Trax Retail (Singapore)

Figure 9: Implementation Results at Optical Center